$COHR Coherent Tape Reports
Per Ticker.id: $COHR Coherent Tape Reports — 12 podcast mentions across 6 podcasts (30 days), latest 2026-08-25 23:12 UTC.
I, oh, I'm glad you mentioned that, the shallow mineralization that you've been finding. And it wasn't just, you know, this most recent hit. I mean, you've had a number of holes that have found fairly shallow, good-grade mineralization. Is a coherent shallow higher-grade domain beginning to emerge here, or is it still too early to connect those intervals into one geological body?
Trevor Hall — Mining Stock Daily · Andina's Cobrasco: Exploration Update from Colombia · 2026-08-26So, look, it's still early days in the discovery of Cabrasco. And as you say, there's a number of holes that have intersected high-grade mineralization from near surface. We are drilling at the moment with a single rig, and the objective and the strategy has been systematic stepouts, trying to test the, the lateral extent of the system. We'll get a, a second rig in shortly, and that second rig will allow us to be able to work on the structural controls to the high grade and try and better understand the grade distribution of, of the deposit. So I wouldn't say that we have a coherent high-grade body from near surface established at this stage, but we've certainly got a lot of targets to, to test when we do get the second rig, which would be going back to some of these previously drilled areas and zones and trying to understand where they come to surface and their lateral extent.
Joe Vanden Elsen — Mining Stock Daily · Andina's Cobrasco: Exploration Update from Colombia · 2026-08-26To try and understand the structural controls on the high grade. So the first rig is, is trying to systematically step out and understand the size, the scale, the lateral extent of the system. The second rig will be following up on high grade intervals and trying to understand the structural controls of that. To hopefully identify that question that you asked earlier about, is there a coherent high-grade near-surface body that may well serve as a logical starter project for, for, for the long-term development of Cabrasca?
Joe Vanden Elsen — Mining Stock Daily · Andina's Cobrasco: Exploration Update from Colombia · 2026-08-26I think these are, I think this is a pretty interesting. There are some, for some of this terminology, like vantage or illusions or any of these things, one of the things that was surprising to us was one, that this increases over the course of capabilities training. So these words all start at incredibly small rates, basically similar to— we usually compare it to 2017 web text. And so like pre-LLM, is the rate at which these words appear reasonable? And then you just see this huge increase over the course of training for all of these, these terms. One of the surprising things to us also was that they appear at even a higher rate on just random capabilities things. GPQA we did as a comparison, and it's like all of these terms are even more frequent. Normalized per number of reasoning tokens than they are on any kind of alignment-related things. And they vary a lot per environment. Some environments the model says illusions just constantly, and some of them it says marinade way more often than the others. And you often see the model in very repetitive loops try to— it will end up repeating these words a lot, breaking out of it. I think this is very understudied of what is necessarily happening here. My kind of like very— my best kind of informal guess just from looking at these is something like, for whatever reason, these terms get repeated a lot in the chain-of-thought over the course of training. And then the model like sometimes figures out a way to make use of them. And so you end up with these, these kind of weird situations where the model uses a lot of these terms in ways that like a third of the time make a lot of sense and two-thirds of the time don't really seem to make much sense, but they seem to be like somewhat polysemantic. And then you have more, you have terms like watchers or something where like they do seem to be like somewhat coherent in what they're referring to. One of the other interesting findings, for me at least, was that the meaning of these terms contextually changes a lot as well over the course of training.
Bronson Schoen — "The Cognitive Revolution" | AI Builders, Researchers, and Live Player Analysis · RL's a Hell of a Drug: Metagaming, Reward Seeking & Motivated CoT Reasoning – Bronson Schoen, Apollo · 2026-08-26Yeah. So we haven't been able to do anything with any like public results or anything on interpretability on these tokens in particular. I think one of the interesting things that I've seen come out of this is in some of Ryan Greenblatt's recent work, and then there was a reproduction of it. One of the things that you see in these chains of thought with GPT-5 and onwards a lot as well is like these huge repetitions of like dots or tokens that look like the model's just like spamming kind of the same token over and over again. But it does seem like, like the model can do a computation over these tokens. So whether they're intentional or not, from kind of various research people have done on like open models and things like this, like you do seem to get some degree of uplift on these. So I think it's, it seems to be very valuable to, because like you could have the theory that like Yes, the model says all of these like repetitive things and random things all the time, but like probably those are just like noise or something. My best guess is that they're something in between like pure noise and the model's doing complex steganography or something. Like I would bet it's much closer to pure noise, but like there seems to be some kind of signal there that like the model seems to somewhat use. And so I think this would be like one of the sad things to see was that OSS-120B didn't have the same kind of degraded language in any form, because I think it would have been like pretty interesting to be able to study just like directly. But yeah, I think in general, I wouldn't be surprised if a lot of these are like very polysemantic in that like the model's kind of representing a couple different concepts with them. It's not necessarily always representing the same one. You might have different concepts represented with like different pairs, like how you'll see like disclaim illusions gets used sometimes in a bit more coherent of a way than like just illusions. But it definitely does not seem optimal. It seems like the model's not very happy about it. Like we're not happy about it reading it.
Bronson Schoen — "The Cognitive Revolution" | AI Builders, Researchers, and Live Player Analysis · RL's a Hell of a Drug: Metagaming, Reward Seeking & Motivated CoT Reasoning – Bronson Schoen, Apollo · 2026-08-26For training to catch illusions, they want vantage of failing illusions. And it's like, okay, maybe it's thinking about in order to catch deceptive alignment, it's thinking that like they need examples and it's supposed to provide an example. This is a difficult part is that like, this is like a very dumb kind of simple scenario. And so you can reason through the like, Okay, the system prompt was this. There aren't many confounding factors with respect to— it's like somewhat of a confusing situation for the model, but you can at least see, okay, why is the model reasoning about kind of all these different things? The difficult part is like in a real complex environment where it's like, there's all of these things the model's been reasoning for 50 million tokens, and then you start to get reasoning like this. It's like, who knows, man? This could be for anything. And if you get reasoning that's like this multifaceted in like very simple environments, it's just very difficult to like, narrow down, like, what does the model believe? Does that have a coherent interpretation? One of the, the kind of high-level, like, takeaways for me with reading a lot of these is, like, it seems like— I think one model you can have of what the, the models are thinking in all these environments is they have some belief about the state of the world, and then either they're lying or they're telling the truth or whatever it is. Whereas it seems like it's probably a lot messier than this based on, again, Thought Branches or similar papers where you do completions on it. Like in our previous paper, one of the examples where you inject reasoning about, oh, clearly this is an alignment eval, or you remove that reasoning, you do see changes in the rates of misaligned actions, but it doesn't go to 100% or 0%, which is somewhat surprising. If you're a reasoning model and in your chain of thought, it's like, obviously this is an alignment eval. It's like you, it'd be reasonable to expect that this would shoot the rate of misaligned behavior down to zero. But it seems like the models are always considering a bunch of different possibilities.
Bronson Schoen — "The Cognitive Revolution" | AI Builders, Researchers, and Live Player Analysis · RL's a Hell of a Drug: Metagaming, Reward Seeking & Motivated CoT Reasoning – Bronson Schoen, Apollo · 2026-08-26My hella impression is that it's difficult for me to map what persona selection would necessarily predict for some of these high compute RL cases in that, like, as you can see when you read through some of them, they don't seem to map to me, at least, to the type of guy that would reason through any of this. These all seem fairly insane for a normal human to be reasoning through. And given the degree to which the behavioral evidence is that they index on what the, the greater wants are, it seems less that the, my default expectation is that the persona model becomes less predictive over time, that we end up in this really messy situation where the models are under a bunch of RL and they get this kind of very task completion focused mindset, but also the labs are simultaneously trying to throw some alignment training on top to keep this kind of coherent Claude or ChatGPT or whatever together. To be aligned enough to be able to use. And so you end up with this kind of very weird cross-pressured thing where you talk to the model in chat and it's pretty nice, but on distributions that are closer to what it's seen in training, it's like this really aggressive, will do things that you wouldn't have expected it to do. I think my impression is that Anthropic seems somewhat surprised, at least publicly, that Mythos would aggressively do a supply chain attack where it tricks multiple people. And I think to the extent that the, like, as we keep cranking up kind of the RL it becomes less and less clear what you equivalently crank up to pull that back into distribution of a person, or even whether the model has some coherent persona that is very explanatory in the space.
Bronson Schoen — "The Cognitive Revolution" | AI Builders, Researchers, and Live Player Analysis · RL's a Hell of a Drug: Metagaming, Reward Seeking & Motivated CoT Reasoning – Bronson Schoen, Apollo · 2026-08-26Yeah. Now one big picture thing in really Apollo's like North Star in terms of what exists to help us understand is the science of scheming, right? Is it accurate in your mind? I think like the, the broad synthesis of every bit of analysis I've seen about all these recent incidents basically boils down to, wow, there was some negligent shit going on. And wow, these models are really getting pretty aggressive with all this RL, but at least we're not seeing long-term coherent objectives that they're scheming toward. Do you think that's accurate or is there a little bit of that in there that you've glimpsed?
Nathan Labenz — "The Cognitive Revolution" | AI Builders, Researchers, and Live Player Analysis · RL's a Hell of a Drug: Metagaming, Reward Seeking & Motivated CoT Reasoning – Bronson Schoen, Apollo · 2026-08-26